Factors affecting thrips (Thysanoptera: Thripidae) population densities in watermelon crops.
Thrips are among the primary pests of the watermelon crop in tropical regions, especially Frankliniella schultzei (Trybom) (Thysanoptera: Thripidae) (Morais et al. 2007; Pereira et al. 2017). Frankliella schultzei is widely distributed, occurring in 136 countries, and attacking 83 plant species belonging to 35 different plant families (Palmer 1990). Thrips adults and nymphs cause damage to plants by sucking out their cell contents, injecting enzymes into the plant that are in their saliva, and acting as vectors of viruses (Mound 1995; Monteiro et al. 2001; Morsello et al. 2008; Riley et al. 2011; Cavalleri & Mound 2012; Costa et al. 2015; Shrestha et al. 2015).
The identification of factors that regulate the intensity of pest attacks on crops is important for pest sampling and control (Picanco et al. 2000, 2002; Rosado et al. 2015). Such studies also permit the establishment of predictive models of pest attacks on crops, so that the farmers can determine the correct time to start pest control (Herms 2004; Rosado et al. 2015; Silva et al. 2016).
Among the principal factors that regulate pest populations on crops are the characteristics of the host plant, weather and climate, and populations of natural enemies (Price et al. 1980). Among the characteristics of the host plant that affect populations of herbivorous insects are phenological stage (Herms 2004), and the age of the plant tissue on which these organisms feed (Joost & Riley 2008; Rosado et al. 2015). The age and phenological stage of the plant on which the insects feed also may affect the nutritional content, as well as the chemical and morphological defenses against arthropod herbivores (Moreira et al. 2016).
The principal elements of weather and climate that affect populations of pests on crops are air temperature, rainfall, wind, and photoperiod (Wellington 1957; Morsello et al. 2008; Rosado et al. 2015). Weather and climate affect the survival, development, reproduction, and dispersal of insects (Wellington 1957; Morsello et al. 2008; Rosado et al. 2015). In tropical regions, the weather and climate features that vary most over the year consist of rainfall intensity and wind speed, whereas air temperature and photoperiod display less variation (Alvares et al. 2013).
The principal natural enemies of insect pests in watermelon crops are predators, with Geocoris sp. (Hemiptera: Geocoridae), Eriopis connexa (Coleoptera: Coccinellidae), and Orius sp. (Hemiptera: Anthocoridae) most frequently observed (Picanco et al. 2007; Lima et al. 2014).
Despite the importance of F. schultzei as a pest of watermelon, and the need to shed light on the factors that determine its damage potential, there has been a lack of research on this subject, especially in tropical regions. The purpose of this study was to determine the factors regulating the population growth of F. schultzei in watermelon plantations in tropical regions. To this end, we evaluated the effect of abiotic (weather and climate) and biotic (phenological stage of leaves and occurrence of natural enemies) factors on F. schultzei population densities on commercial watermelon crops in a tropical region over a period of 2 yr.
Materials and Methods
This study was conducted during 2014 and 2015 in commercial watermelon fields in Formoso do Araguaia (11.902106[degrees]S, 49.561603[degrees]W, with an altitude of 240 masl, and a tropical climate with a dry winter and rainy summer) in Tocantins State, Brazil. The study covered 2 seasons of watermelon cultivation in these yr, which runs from May to Aug (the dry season) and from Jan to Apr (the rainy season) for each yr. The fields were established according to Santos & Zambolim (2011), and the chosen spacing was 2.80 m between rows and 1.45 m between plants. The fields had an area of approximately 15 ha. Specimens of thrips were collected at each evaluation time, and taken to the laboratory for later identification using taxonomic keys and morphological characterization according to Palmer et al. (1990) and Monteiro et al. (2001). Weather data were monitored daily by the central weather station of the National Institute of Meteorology in Formoso do Araguaia, located at the same elevation as the experimental field. The air temperature ([degrees]C), wind velocity (m per s), photoperiod (h), and rain (mm per day) were recorded hourly.
This research was divided into 2 parts. In the first part, we evaluated the variation in the abundance of F. schultzei in relation to leaf position, as well as the phenological stage of the plants. In the second part, we evaluated the abundance of F. schultzei in relation to the occurrence of natural enemies in 2 seasons of watermelon cultivation.
Abundance of Frankliniella schultzei Relative to Leaf Position and Phenological Stage of Plants
This study was carried out in 5 commercial watermelon fields (cultivar Manchester, Isla Superpak). In each field, 100 plants were evaluated for each plant growth stage, totalling 300 plants. Plants were randomly selected and 1 vine of each plant was selected. Subsequently, the density of the larvae and adults of F. schultzei were recorded on each leaf of the vine to verify the choice of the thrips in relation to the position of the leaf, and in relation to the phenological stage of the plants. The most apical leaf on the vine was labelled number 1, the second most apical leaf was labelled number 2, and so on, until the base of the plant was reached. Three assessments were carried out as follows: the first was undertaken on plants at the vegetative stage (40 days after planting, before the appearance of the first flower); the second on flowering plants (between the appearance of the first flower and the development of first fruit); and the third on fruiting plants (after the formation of the first fruit). Frankliniella schultzei densities were evaluated by visual examination and direct count, because this is the best technique for determining the abundance of this pest in watermelon crops (Pinto et al. 2017). During the evaluations, the leaves were carefully handled to prevent escape of thrips.
The data on F. schultzei densities in relation to leaf position and phenological stage were subjected to regression analysis. The selection of the regression curve was based on its significance (P < 0.05), the coefficient of regression ([R.sup.2]), and the simplicity of the equation (Johnson & Omland 2004). All analyses were performed using SAS Version 8.1 (2002) (SAS Institute, Cary, North Carolina).
Densities of Frankliniella schultzei and Natural Enemies in Relation to Season
This work was carried out in 8 watermelon commercial fields cultivated in the dry and rainy seasons during 2014 and 2015, as previously described. These 2 seasons (periods) were chosen because they are the normal watermelon cultivation periods in tropical regions such as Brazil (Santos & Zambolim 2011), and they experience the highest variation in weather (Alvares et al. 2013). The densities of F. schultzei and natural enemies were assessed in 50 samples per field. Each sample consisted of 5 watermelon plants. To eliminate possible directional trends, the plants assessed were located equidistantly in each row and between rows; therefore reflecting systematic sampling points (Bacci et al. 2008).
Natural enemies were sampled using the visual examination and direct count techniques, as used for thrips. Specimens collected were classified into morphospecies and stored in glass bottles (10 mL) containing 70% ethanol, for later identification. These morphospecies were identified using taxonomic keys (Picanco et al. 2007), and compared to the Regional Museum of Entomology collection at the Federal University of Vicosa, Minas Gerais State, Brazil.
Frankliniella schultzei and natural enemy densities data as a function of the cropping season were analyzed by ANOVA at P < 0.05. Daily averages and standard errors of the weather data (air temperature [[degrees]C], wind speed [m per s], photoperiod [hours], and rainfall [mm per day]) were determined. Stepwise multiple regression analyses were performed to identify the most important weather, natural enemy, and plant phenological stage variables that influence the abundance of F. schultzei in watermelon. Each yr was considered a replicate. The independent variables for the analysis were the climatic elements, densities of natural enemies, and plant phenological stages data, and the dependent variable of interest was the F. schultzei density per leaf. In this model, the plant stages were represented by the following numbers: 1 (vegetation), 2 (flowering), and 3 (fruiting). Natural enemies (spiders, Chrysoperla sp. and Geocoris sp.) and 2 of the weather and climate variables (mean rainfall and wind speeds) were used in this regression model because they were different between the 2 growing seasons. All analyses were performed using SAS Version 8.1 (SAS Institute, Cary, North Carolina).
The abundance of F. schultzei was affected by the phenological stages of the plant and the position of the leaf on the vine. The highest densities of thrips were observed during the vegetative stage, while the densities were intermediate during flowering, and lowest in the fruiting plants (Fig. 1).
In the vegetative and flowering stages the density of thrips decreased from the apex to the base of the vines (F = 24.04; df = 1,7; P = 0.0018) (Figs. 1A, B). During the fruiting stage, the density of the pest was higher on apical leaves than on the other leaves (F = 158.80; df = 1,19; P = 0.0001) (Fig. 1C).
The densities of adults observed were higher than that of nymphs of F. schultzei in all plant growth stages (ratio of 10:1 adults:nymphs). The natural enemies found in watermelon fields consisted only of predators: various spiders, Eriopis connexa (German) (Coleoptera: Coccinellidae), Chrysoperla sp. (Neuroptera: Chrysopidae), Geocoris sp. (Hemiptera: Geocoridae), and Orius sp. (Hemiptera: Anthocoridae). The descending order of predator densities observed in watermelon crops were: Geocoris sp. > Chrysoperla sp. > spiders > E. connexa > Orius sp. (Table 1).
Frankliniella schultzei and predator densities varied according to the planting season. During the 2 planting seasons, F. schultzei densities were higher than the predator densities. The densities of Chrysoperla sp. ([F.sub.1,398] = 9.26; P = 0.0025) and Geocoris sp. ([F.sub.1,398] = 15.19; P < 0.001) were higher in the dry season, whereas the spider density was higher in the rainy season than in the dry season ([F.sub.1,398] = 4.83; P = 0.028), and the densities of Orius sp. ([F.sub.1,398] = 1.00; P = 0.317) and E. connexa ([F.sub.1,398] = 2.79; P = 0.095) were similar in the 2 growing seasons. The densities of F. schultzei and total predators to watermelon plants was higher in the dry season than in the rainy season (F schultzei [[F.sub.1,398] = 29.6; P < 0.0001]; total predators [[F.sub.1,398] = 13.95; P< 0.0001]) (Fig. 2).
Mean temperatures remained high and similar for both cropping seasons. Similarly, the photoperiod did not vary between the 2 growing seasons. The rainfall was higher in the rainy season, and the wind speeds were higher in the dry season (Fig. 3).
The multiple linear regression model of the density of F. schultzei in watermelon culture varied in relation to the phenological stage of plants, rainfall (mm per day), wind speed (m per s), and density of the predators Chrysoperla sp., spiders, and Geocoris sp. was significant (P = 0.032). This model explained 69% of the variation in the density on F. schultzei in the watermelon fields studied. In this model, the angular coefficients of the effects of phenological stage of plants and rainfall on F. schultzei densities were negative. On the other hand, the angular coefficients from the effects of wind speed and density of the predator Chrysoperla sp. on F. schultzei densities were positive. The angular coefficient of the effects of spiders and Geocoris sp. densities on F. schultzei density were not significant (P > 0.05) (Table 2).
We observed the highest densities of F. schultzei on watermelon leaves in the vegetative stage. In this stage, plants provide large numbers of new leaves (Braga et al. 2011). Another fact that reinforces this statement was the presence of higher densities of F. schultzei on younger leaves, which were located on the most apical parts of the vine. This was observed in watermelon plants at all 3 phenological stages (vegetative, flowering, and fruiting).
The preference of F. schultzei for younger leaves might be related to the higher nutritional quality of these leaves; for example, the presence of high concentrations of protein, carbohydrates, and vitamins (Bernays & Chapman 1994; Gurevitch et al. 2006; Newton et al. 2009). Higher nutrient concentrations in young leaves compared to older leaves is due to the nutrient translocation (especially nitrogen) from older to younger leaves (Mattson 1980; Joost & Riley 2008; Barker & Pilbeam 2015; Mengel 2015). In addition, older leaves often have a tough epidermis, as well as larger trichomes (Leite et al. 2004), which may prevent insects from feeding on them (Scott Brown & Simmonds 2006). Such plant defenses reduce the ability of thrips to penetrate the leaf and remove the sap (Milne & Walter 2000). This could explain the preference of the thrips for younger leaves.
An important factor regulating pest populations in crops is weather and climate (Wellington 1957; Semeao et al. 2012). In this context, we observed higher populations of F. schultzei in watermelon crops in the dry season than in the rainy season. In the multiple regression analysis, we observed a negative impact of rainfall on F. schultzei populations. Rainfall may affect pest populations in direct and indirect ways (Pereira et al. 2007; Morsello et al. 2008; Semeao et al. 2012). In a direct way, rainfall causes insect death due to the mechanical impact of droplets which wash small insects down onto the soil (Semeao et al. 2012). Indirectly, rainfall may have both negative and positive effects on insect herbivorous population. The indirect negative effect is due to the increase in humidity, which increases insect mortality by entomopathogenic fungi (Augustyniuk-Kram & Kram 2012). The indirect positive effect of rainfall is due to the increase in water available to the plants, which become a food resource to herbivorous insects; this is probably what happened to F. schultzei in our experiments (Floater 1997).
In the multiple regression model, we observed that winds showed a positive correlation with F. schultzei populations in watermelon crops. It is known that thrips do not have the ability to fly long distances (Gatehouse 1997); however, they can be dispersed rapidly by winds (Pelikan 1989), and thus may move long distances (Mound 1983). This was observed by Pearsall and Myers (2001), who verified that another thrips species (Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae) disperses in the wind direction in nectarine orchards.
In the multiple regression model of the factors that affected F. schultzei density on watermelon plants, we found a positive correlation between pest density and occurrence of the predator Chrysoperla sp. Therefore, we favor the hypothesis that the high densities of thrips found in our study might explain the increased populations of Chrysoperla sp. on watermelon plants. This higher availability of food results in the increased reproduction rate and survival of natural enemies, as well as the migration of such insects to the surrounding areas of crops.
In conclusion, the study presented here contributes to an understanding of the factors regulating the attack of thrips F. schultzei in watermelon crops. The intensity of F. schultzei attack depends on the phenological stage of plants, weather and climate, and natural enemy populations. Frankliniella schultzei are more abundant during dry periods when winds are relatively higher. Frankliniella schultzei populations also are higher in the vegetative stage, and on young leaves of watermelon plants. Chrysoperla sp. may be an important natural enemy of F. schultzei in watermelon fields.
We thank the National Council for Scientific and Technological Development-CNPq, Brazil (Projects: 458946/2014-1 and 304178/2015-2), the Coordination for the Improvement of Higher Education Personnel-CAPES, Brazil (Project: PROCAD-NF AUXPE NF 187/2010), and the Minas Gerais State Research Foundation-FAPEMIG, Brazil, for the scholarships and resources provided.
Alvares CA, Stape JL, Sentelhas PC, Moraes G, Leonardo J, Sparovek G. 2013. Koppen's climate classification map for Brazil. Meteorologische Zeitschrift 22: 711-728.
Augustyniuk-Kram A, Kram KJ. 2012. Entomopathogenic fungi as an important natural regulator of insect outbreaks in forests (Review), pp. 265-294 In Blanco JA, Lo Y [eds.], Forest Ecosystems-More Than Just Trees. InTech, Rijeka, Poland.
Bacci L, Picanco MC, Moura MF, Semeao AA, Fernandes FL, Morais EG. 2008. Sampling plan for thrips (Thysanoptera: Thripidae) on cucumber. Neotropical Entomology 37: 582-590.
Barker AV, Pilbeam DJ [eds.]. 2015. Handbook of Plant Nutrition. CRC Press, Taylor and Francis, Boca Raton, Florida, USA.
Bernays EA, Chapman RF [eds.]. 1994. Hostplant Selection by Phytophagous Insects. Springer Science and Business Media, New York, USA.
Braga DF, Negreiros MZ, Freitas FCL, Grangeiro LC, Lopes WDAR. 2011. Crescimento de melancia 'mickylee' cultivada sob fertirrigacao. Revista Caatinga 24: 49-55.
Cavalleri A, Mound LA. 2012. Toward the identification of Frankliniella species in Brazil (Thysanoptera, Thripidae). Zootaxa 3270: 1-30.
Costa EM, Lima MGAD, Junior RS, Cavalleri A, Araujo EL. 2015. Thrips collected in watermelon crops in the semiarid of Rio Grande do Norte, Brazil. Ciencia Rural 45: 575-577.
FAO (Food and Agriculture Organization of the United Nations). 2014. FAO-Food and nutrition in numbers-2014. http://www.fao.org/home/en/ (last accessed 4 Sep 2016).
Floater G. 1997. Rainfall, nitrogen and host plant condition: consequences for the processionary caterpillar, Ochrogaster lunifer. Ecological Entomology 22: 247-255.
Gatehouse AG. 1997. Behavior and ecological genetics of wind-borne migration by insects. Annual Review Entomology 42: 475-502.
Gurevitch J, Scheiner SM, Fox GA [eds.]. 2006. The Ecology of Plants. Sinauer Associates, Sunderland, Oxford, United Kingdom.
Herms DA. 2004. Using degree-days and plant phenology to predict pest activity, pp. 49-59 In Krischik V, Davidson J [eds.], IPM (Integrated Pest Management) of Midwest Landscapes. University of Minnesota, Menneapolis, Minnesota, USA.
Johnson JB, Omland KS. 2004. Model selection in ecology and evolution. Trends in Ecology & Evolution 19: 101-108.
Joost PH, Riley DG. 2008. Tomato plant and leaf age effects on the probing and settling behavior of Frankliniella fusca and Frankliniella occidentalis (Thysanoptera: Thripidae). Environmental Entomology 37: 213-223.
Leite GLD, Picanco MC, Jham GN, Marquini F. 2004. Intensity of Tuta absoluta (Meyrick, 1917) (Lepidoptera: Gelechiidae) and Liriomyza spp. (Diptera: Agromyzidae) attacks on Lycopersicum esculentum Mill. leaves. Ciencia e Agrotecnologia 28: 42-48.
Lima CHO, Sarmento RA, Rosado JF, Silveira MCAC, Santos GR, Pedro Neto M, Picanco MC. 2014. Efficiency and economic feasibility of pest control systems in watermelon cropping. Journal of Economic Entomology 107: 1118-1126.
Mattson Jr WJ. 1980. Herbivory in relation to plant nitrogen content. Annual Review of Ecology and Systematics 11: 119-161.
Mengel K. 2015. Potassium, pp. 91-120 In Barker AV, Pilbeam DJ [eds.], Handbook of Plant Nutrition. CRC Press, Taylor and Francis, Boca Raton, Florida, USA.
Milne M, Walter GH. 2000. Feeding and breeding across host plants within a locality by the widespread thrips Frankliniella schultzei, and the invasive potential of polyphagous herbivores. Diversity and Distributions 6: 243-257.
Monteiro RC, Mound LA, Zucchi RA. 2001. Species of Frankliniella (Thysanoptera: Thripidae) as pests in Brazil. Neotropical Entomology 30: 65-72.
Morais ED, Picanco MC, Sena ME, Bacci L, Silva GA, Campos MR. 2007. Identificacao das principais pragas de hortalicas no Brasil, pp. 381-422 In Zambo-lim L, Lopes CA, Picanco MC, Costa H [eds.], Manejo Integrado de Doencas e Pragas-Hortalicas. Universidade Federal de Vicosa, Vicosa, Minas Gerais, Brazil.
Moreira LF, Teixeira NC, Santos NA, Valim JOS, Mauricio RM, Guedes RNC, Oliveira MGA, Campos WG. 2016. Diamondback moth performance and preference for leaves of Brassica oleracea of different ages and strata. Journal of Applied Entomology 140: 627-635.
Morsello SC, Groves RL, Nault BA, Kennedy GG. 2008. Temperature and precipitation affect seasonal patterns of dispersing tobacco thrips, Frankliniella fusca, and onion thrips, Thrips tabaci (Thysanoptera: Thripidae) caught on sticky traps. Environmental Entomology 37: 79-86.
Mound LA. 1983. Natural and disrupted patterns of geographical distribution in Thysanoptera (Insecta). Journal of Biogeography 10: 119-133.
Mound LA. 1995. The Thysanoptera vector species of tospoviruses. ISHS Acta Horticulture 431: 298-309.
Newton EL, Bullock JM, Hodgson DJ. 2009. Glucosinolate polymorphism in wild cabbage (Brassica oleracea) influences the structure of herbivore communities. Oecologia 160: 63-76.
Palmer JM. 1990. Identification of the common thrips of tropical Africa (Thysanoptera: Insecta). International Journal of Pest Management 36: 27-49.
Pearsall IA, Myers JH. 2001. Spatial and temporal patterns of dispersal of western flower thrips (Thysanoptera: Thripidae) in nectarine orchards in British Columbia. Journal of Economic Entomology 94: 831-843.
Pelikan J. 1989. A new imported pest of greenhouse plants, the western flower thrips, Frankliniella occidentalis (Pergande, 1895). Ochrana Rostlin 25: 271-278.
Pereira EJG, Picanco MC, Bacci L, Della Lucia TMC, Silva EM, Fernandes FL. 2007. Natural mortality factors of Leucoptera coffeella (Lepidoptera: Lyonetiidae) on Coffea arabica. Biocontrol Science and Technology 17: 441-455.
Pereira PS, Sarmento RA, Galdino TVS, Lima CHO, Santos FA, Silva J, Picanco MC. 2017. Economic injury levels and sequential sampling plans for Frankliniella schultzei in watermelon crops. Pest Management Science 73: 1438-1445.
Picanco MC, Giroldo AS, Bacci L, Morais EGF, Silva GA, Sena MEF. 2007. Controle biologico das principais pragas de hortaligas no Brasil, pp. 505-538 In Zambolim L, Lopes CA, Picanco MC, Costa H [eds.], Manejo Integrado de Doencas e Pragas - Hortalicas. Universidade Federal de Vicosa, Vicosa, Minas Gerais, Brazil.
Picanco MC, Gusmao MR, Galvan TL. 2000. Manejo integrado de pragas de hortaligas, pp. 275-324 In Zambolim L [eds.], Manejo Integrado de Doencas, Pragas e Ervas Daninhas. Universidade Federal de Vicosa, Vicosa, Minas Gerais, Brazil.
Picanco MC, Pereira EJG, Crespo ALB, Semeao AA, Bacci L. 2002. Manejo integrado das pragas das fruteiras tropicais, pp. 513-578 In Zambolim L [eds.], Manejo Integrado: Fruteiras Tropicais Doengas e Pragas. Universidade Federal de Vigosa, Vigosa, Minas Gerais, Brazil.
Pinto CB, Sarmento RA, Galdino TVS, Pereira PS, Barbosa BG, Lima CHO, Silva NR, Picanco MC. 2017. Standardised sampling plan for the thrips Frankliniella schultzei on watermelon crops. Journal of Economic Entomology 110: 748-754.
Price PW, Bouton CE, Gross P, McPheron BA, Thompson JN, Weis AE. 1980. Interactions among three trophic levels: influence of plants on interactions between insect herbivores and natural enemies. Annual Review of Ecology and Systematics 11: 41-65.
Riley DG, Joseph SV, Srinivasan R, Diffie S. 2011. Thrips vectors of tospoviruses. Journal of Integrated Pest Management 2: I1-I10.
Rosado JF, Picanco MC, Sarmento RA, Silva RS, Pedro-Neto M, Carvalho MA, Silva LCR. 2015. Seasonal variation in the populations of Polyphagotarsonemus latus and Tetranychus bastosi in physic nut (Jatropha curcas) plantations. Experimental and Applied Acarology 66: 415-426.
Santos GR, Zambolim L [eds.]. 2011. Tecnologia para Producao Sustentavel da Melancia no Brasil. Universidade Federal de Vicosa, Vicosa, Minas Gerais, Brazil.
SAS Institute. 2002. PROC user's manual, version 8.1. SAS Institute, Cary, North Carolina, USA.
Scott Brown AS, Simmonds MS. 2006. Leaf morphology of hosts and nonhosts of the thrips Heliothrips haemorrhoidalis (Bouche). Botanical Journal of the Linnean Society 152: 109-130.
Semeao AA, Martins JC, Picanco MC, Bruckner CH, Bacci L, Rosado JF. 2012. Life tables for the guava psyllid Triozoida limbata in southeastern Brazil. BioControl 57: 779-788.
Shrestha A, Sundaraj S, Culbreath AK, Riley DG, Abney MR, Srinivasan R. 2015. Effects of thrips density, mode of inoculation, and plant age on tomato spotted wilt virus transmission in peanut plants. Environmental Entomology 44: 136-143.
Silva RS, Kumar L, Shabani F, Silva EM, Galdino TVS, Picanco MC. 2016. Spatio-temporal dynamic climate model for Neoleucinodes elegantalis using CLIMEX. International Journal of Biometeorology 60: 1-11.
Wellington WG. 1957. The synoptic approach to studies of insects and climate. Annual Review of Entomology 2: 143-162.
Breno Gomes Barbosa (1), Renato Almeida Sarmento (2*), Poliana Silvestre Pereira (2), Cleovan Barbosa Pinto (1), Carlos Henrique de Oliveira Lima (2), Tarcisio Visintin da Silva Galdino (1), Abraao Almeida Santos (1), and Marcelo Coutinho Picanco (1)
(1) Departamento de Entomologia, Universidade Federal de Vicosa, Av. Peter Henry Rolfs, 36570-000, Vicosa, Minas Gerais, Brazil; E-mails: email@example.com (B. G. B.); firstname.lastname@example.org (C. B. P.); email@example.com (T. V. S. G.); firstname.lastname@example.org (A. A. S.); email@example.com (M. C. P.) (2) Universidade Federal do Tocantins, Campus de Gurupi, 77402-970, Gurupi, Tocantins, Brazil; E-mails: firstname.lastname@example.org (R. A. S.); email@example.com (P. S. P.); firstname.lastname@example.org (C. H. O. L.)
(*) Corresponding author; E-mail: email@example.com
Caption: Fig. 1. Frankliniella schultzei density depending on the position of the leaf on the branch in watermelon plants in vegetative (A), flowering (B), and (C) fruiting stages. The more apical leaf branch was considered number 1, the second number 2, and so on.
Caption: Fig. 2. Frankliniella schultzei and predator densities (mean [+ or -] standard error) in 2 seasons of watermelon cultivation. *When a pair of histograms is topped by the same letter, the average densities of this arthropod did not differ in the 2 seasons of cultivation according to the F test and P < 0.05.
Caption: Fig. 3. Daily average (mean [+ or -] standard error) of air temperature, wind speed, photoperiod, and rain during 2 seasons of watermelon cultivation.
Table 1. Densities of thrips pests and predators in watermelon crops. Taxon Densities (*) (individuals per [sample.sup.-1]) Frankliniella schultzei Nymphs 0.33 [+ or -] 0.04 Adults 4.87 [+ or -] 0.26 Total 5.20 [+ or -] 0.27 Predators Spiders 0.10 [+ or -] 0.014 Eriopis connexa 0.03 [+ or -] 0.008 Chrysoperla sp. 0.11 [+ or -] 0.022 Geocoris sp. 0.17 [+ or -] 0.023 Orius sp. 0.006 [+ or -] 0.004 Predators total 0.41 [+ or -] 0.04 (*) The samples were made up of 5 leaves. Table 2. Angular coefficients of multiple linear regression of Frankliniella schultzei density according to the phenological stage of watermelon plants, weather elements, and densities of predators. Independent variable Angular coefficients of multiple linear regression Phenological stage of plants -0.35 (*) Climatic elements Rainfall (mm per [day.sup.-1]) -0.33 (*) Average speed of the winds (m per [s.sup.-1]) 0.66 (*) Predators Spiders per [sample.sup.-1] 0.21 Chrysoperla sp. [sample.sup.-1] 0.50 (*) Geocoris sp. [sample.sup.-1] -0.05 Characteristics of model [R.sup.2] 0.69 F 5.58 P 0.032 (*) Significant coefficients by F test and P < 0.05.
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|Author:||Barbosa, Breno Gomes; Sarmento, Renato Almeida; Pereira, Poliana Silvestre; Pinto, Cleovan Barbosa;|
|Date:||Mar 1, 2019|
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